Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera, https://github.com/huydhn
Headers under torch/csrc/distributed may be referened with relative path, e.g., "<c10d/...>". However, relative path cannot be gracefully handled by Meta internal build when the NCCL PG is hipified to support AMD/RCCL because the "hipified" header files are generated in other directories. Moreover, using absolute path for header inclusion is the state-of-the-art in most components in Pytorch. Thus, this patch refactors all header paths in torch/csrc/distributed to be absolute.
See D39835774 for more details about Meta internal complication.
**How to test**: commit 9e5d199 removes -I./torch/csrc/distributed in compile options. Thus use it to verify we don't miss any relative path use of torch/csrc/distributed headers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85780
Approved by: https://github.com/kumpera
### Changes
- Move ProcessGroup::Work into its own class and update all the references to it / header includes.
#### Motivation
In the future PRs we will repurpose ProcessGroup to instead contain a list of Backends (ProcessGroupNCCL/Gloo/UCC) and perform dispatching to them based on tensor type. This change is prevent a circular dependency with ProcessGroup depending on Backend and Backend depending on ProcessGroup::Work.
Differential Revision: [D38839212](https://our.internmc.facebook.com/intern/diff/D38839212)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83680
Approved by: https://github.com/kwen2501
Previously, when we SymInt-ify a schema, this is a BC-breaking change
for all people who registered functions for that function; they
must accept c10::SymInt where they previously accepted int64_t.
This is not great.
With this change, I accept old type registrations transparently. The
idea is in several parts:
- At the registration site, at compile time I have no idea whether or not
if the function being registered has a SymInt schema or not. So I
must defer the exact compatibility check. What I do instead is
check if the function pointer registered to me has SymInt in the
argument or not. If it does, I assume it is new-style and ensure
it is also registered to a special sym_ slot on KernelFunction.
If not, it only goes in the conventional slot.
- At the dispatcher site, I know at compile time whether or not this
is a SymInt function. If it is, I check for a sym_ slot on the
KernelFunction, and preferentially use that. If no such slot
exists, I then fall back to the regular slot... but I convert
all SymInt arguments to int64_t arguments (doing assertions that
no true symbolic integer was passed.) I can skip this test entirely
if the function doesn't have any SymInts in it; in that case I know
that only the original slot could have been registered. Fortunately,
both branches of the short circuit typecheck, so I didn't have to
use SFINAE or if-constexpr to make it work; just a plain if statement
that I expect the compiler to optimize away.
- Schema validation is now modestly more complicated. There are two parts. First, function schema validation proceeds by checking if the signature in question has any SymInt-like types in it or not. If it does, we do function schema validation against the real types; if it doesn't, we do validation against the fake types (but only for symint; MemoryFormat is always MemoryFormat). Second, cpp signature validation also keeps track of a "symint" cpp signature and a "non-symint" cpp signature. We only compare symint with symint, and non-symint with non-symint. I did not implement checking a conflict between a symint and non-symint cpp signature, though in principle you could try converting the SymInt types to non-SymInt types and doing the comparison that way.
To show it is working, I remove a bunch of c10::asIntArrayRefSlow shims, as the dispatcher is able to insert them automatically now.
I didn't update the Metal registrations (though they can get similar treatment) as OSS CI coverage is insufficient for this case.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: [D39280965](https://our.internmc.facebook.com/intern/diff/D39280965)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84557
Approved by: https://github.com/wconstab
Also Back out "Revert D39075159: [acc_tensor] Use SymIntArrayRef for overloaded empty.memory_format's signature"
Original commit changeset: dab4a9dba4fa
Original commit changeset: dcaf16c037a9
Original Phabricator Diff: D38984222
Original Phabricator Diff: D39075159
Also update Metal registrations for C++ registration changes.
Also update NNPI registration to account for tightened schema checking
Differential Revision: [D39084762](https://our.internmc.facebook.com/intern/diff/D39084762/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39084762/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84173
Approved by: https://github.com/Krovatkin
Previously, we introduced new SymInt overloads for every function we wanted. This led to a lot of boilerplate, and also a lot of confusion about how the overloads needed to be implemented.
This PR takes a simpler but more risky approach: just take the original function and changes its ints to SymInts.
This is BC-breaking in the following ways:
* The C++ API for registering implementations for aten operators will change from int64_t to SymInt whenever you make this change. Code generated registrations in PyTorch do not change as codegen handles the translation automatically, but manual registrations will need to follow the change. Typically, if you now accept a SymInt where you previously only took int64_t, you have to convert it back manually. This will definitely break XLA, see companion PR https://github.com/pytorch/xla/pull/3914 Note that not all dispatch keys get the automatic translation; all the composite keys and Meta keys are modified to take SymInt directly (because they should handle them directly), and so there are adjustments for this.
This is not BC-breaking in the following ways:
* The user facing C++ API remains compatible. Even if a function changes from int to SymInt, the default C++ binding still takes only ints. (e.g., at::empty(IntArrayRef, ...). To call with SymInts, you must call at::empty_symint instead. This involved adding two more signatures to CppSignatureGroup; in many cases I refactored code to iterate over all signatures in the group instead of hard-coding the two that previously existed.
* This is TorchScript compatible; internally we treat SymInts as ints so there is no change to what happens at runtime in TorchScript. In particular, it's OK to reference an empty schema by its old type (using int types), as long as you're not doing string equality (which you shouldn't be), these parse to the same underyling type.
Structure of the PR:
* The general strategy of this PR is that, even when you write `SymInt` inside `native_functions.yaml`, sometimes, we will treat it *as if* it were an `int`. This idea pervades the codegen changes, where we have a translation from SymInt to c10::SymInt or int64_t, and this is controlled by a symint kwarg which I added and then audited all call sites to decide which I wanted. Here are some of the major places where we pick one or the other:
* The C++ FunctionSchema representation represents `SymInt` as `int`. There are a few places we do need to know that we actually have a SymInt and we consult `real_type()` to get the real type in this case. In particular:
* When we do schema validation of C++ operator registration, we must compare against true schema (as the C++ API will provide `c10::SymInt`, and this will only be accepted if the schema is `SymInt`. This is handled with cloneWithRealTypes before we check for schema differences.
* In `toIValue` argument parsing, we parse against the true schema value. For backwards compatibility reasons, I do still accept ints in many places where Layout/SymInt/etc were expected. (Well, accepting int where SymInt is expected is not BC, it's just the right logic!)
* In particular, because SymInt never shows up as type() in FunctionSchema, this means that we no longer need a dedicated Tag::SymInt. This is good, because SymInts never show up in mobile anyway.
* Changes to functorch/aten are mostly about tracking changes to the C++ API registration convention. Additionally, since SymInt overloads no longer exist, registrations for SymInt implementations are deleted. In many cases, the old implementations did not properly support SymInts; I did not add any new functionality with this PR, but I did try to annotate with TODOs where this is work to do. Finally, because the signature of `native::` API changed from int to SymInt, I need to find alternative APIs for people who were directly calling these functions to call. Typically, I insert a new dispatch call when perf doesn't matter, or use `at::compositeexplicitautograd` namespace to handle other caes.
* The change to `make_boxed_from_unboxed_functor.h` is so that we accept a plain IntList IValue anywhere a SymIntList is expected; these are read-only arguments so covariant typing is OK.
* I change how unboxing logic works slightly. Previously, we interpret the C++ type for Layout/etc directly as IntType JIT type, which works well because the incoming IValue is tagged as an integer. Now, we interpret the C++ type for Layout as its true type, e.g., LayoutType (change to `jit_type.h`), but then we accept an int IValue for it anyway. This makes it symmetric with SymInt, where we interpret the C++ type as SymIntType, and then accept SymInt and int IValues for it.
* I renamed the `empty.names` overload to `empty_names` to make it less confusing (I kept mixing it up with the real empty overload)
* I deleted the `empty.SymInt` overload, which ended up killing a pile of functions. (This was originally a separate PR but the profiler expect test was giving me grief so I folded it in.)
* I deleted the LazyDynamicOpsTest tests. These were failing after these changes, and I couldn't figure out why they used to be passing: they make use of `narrow_copy` which didn't actually support SymInts; they were immediately converted to ints.
* I bashed LTC into working. The patches made here are not the end of the story. The big problem is that SymInt translates into Value, but what if you have a list of SymInt? This cannot be conveniently represented in the IR today, since variadic Values are not supported. To work around this, I translate SymInt[] into plain int[] (this is fine for tests because LTC dynamic shapes never actually worked); but this will need to be fixed for proper LTC SymInt support. The LTC codegen also looked somewhat questionable; I added comments based on my code reading.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83628
Approved by: https://github.com/albanD, https://github.com/bdhirsh
Previously, we introduced new SymInt overloads for every function we wanted. This led to a lot of boilerplate, and also a lot of confusion about how the overloads needed to be implemented.
This PR takes a simpler but more risky approach: just take the original function and changes its ints to SymInts.
This is BC-breaking in the following ways:
* The C++ API for registering implementations for aten operators will change from int64_t to SymInt whenever you make this change. Code generated registrations in PyTorch do not change as codegen handles the translation automatically, but manual registrations will need to follow the change. Typically, if you now accept a SymInt where you previously only took int64_t, you have to convert it back manually. This will definitely break XLA, see companion PR https://github.com/pytorch/xla/pull/3914 Note that not all dispatch keys get the automatic translation; all the composite keys and Meta keys are modified to take SymInt directly (because they should handle them directly), and so there are adjustments for this.
This is not BC-breaking in the following ways:
* The user facing C++ API remains compatible. Even if a function changes from int to SymInt, the default C++ binding still takes only ints. (e.g., at::empty(IntArrayRef, ...). To call with SymInts, you must call at::empty_symint instead. This involved adding two more signatures to CppSignatureGroup; in many cases I refactored code to iterate over all signatures in the group instead of hard-coding the two that previously existed.
* This is TorchScript compatible; internally we treat SymInts as ints so there is no change to what happens at runtime in TorchScript. In particular, it's OK to reference an empty schema by its old type (using int types), as long as you're not doing string equality (which you shouldn't be), these parse to the same underyling type.
Structure of the PR:
* The general strategy of this PR is that, even when you write `SymInt` inside `native_functions.yaml`, sometimes, we will treat it *as if* it were an `int`. This idea pervades the codegen changes, where we have a translation from SymInt to c10::SymInt or int64_t, and this is controlled by a symint kwarg which I added and then audited all call sites to decide which I wanted. Here are some of the major places where we pick one or the other:
* The C++ FunctionSchema representation represents `SymInt` as `int`. There are a few places we do need to know that we actually have a SymInt and we consult `real_type()` to get the real type in this case. In particular:
* When we do schema validation of C++ operator registration, we must compare against true schema (as the C++ API will provide `c10::SymInt`, and this will only be accepted if the schema is `SymInt`. This is handled with cloneWithRealTypes before we check for schema differences.
* In `toIValue` argument parsing, we parse against the true schema value. For backwards compatibility reasons, I do still accept ints in many places where Layout/SymInt/etc were expected. (Well, accepting int where SymInt is expected is not BC, it's just the right logic!)
* In particular, because SymInt never shows up as type() in FunctionSchema, this means that we no longer need a dedicated Tag::SymInt. This is good, because SymInts never show up in mobile anyway.
* Changes to functorch/aten are mostly about tracking changes to the C++ API registration convention. Additionally, since SymInt overloads no longer exist, registrations for SymInt implementations are deleted. In many cases, the old implementations did not properly support SymInts; I did not add any new functionality with this PR, but I did try to annotate with TODOs where this is work to do. Finally, because the signature of `native::` API changed from int to SymInt, I need to find alternative APIs for people who were directly calling these functions to call. Typically, I insert a new dispatch call when perf doesn't matter, or use `at::compositeexplicitautograd` namespace to handle other caes.
* The change to `make_boxed_from_unboxed_functor.h` is so that we accept a plain IntList IValue anywhere a SymIntList is expected; these are read-only arguments so covariant typing is OK.
* I change how unboxing logic works slightly. Previously, we interpret the C++ type for Layout/etc directly as IntType JIT type, which works well because the incoming IValue is tagged as an integer. Now, we interpret the C++ type for Layout as its true type, e.g., LayoutType (change to `jit_type.h`), but then we accept an int IValue for it anyway. This makes it symmetric with SymInt, where we interpret the C++ type as SymIntType, and then accept SymInt and int IValues for it.
* I renamed the `empty.names` overload to `empty_names` to make it less confusing (I kept mixing it up with the real empty overload)
* I deleted the `empty.SymInt` overload, which ended up killing a pile of functions. (This was originally a separate PR but the profiler expect test was giving me grief so I folded it in.)
* I deleted the LazyDynamicOpsTest tests. These were failing after these changes, and I couldn't figure out why they used to be passing: they make use of `narrow_copy` which didn't actually support SymInts; they were immediately converted to ints.
* I bashed LTC into working. The patches made here are not the end of the story. The big problem is that SymInt translates into Value, but what if you have a list of SymInt? This cannot be conveniently represented in the IR today, since variadic Values are not supported. To work around this, I translate SymInt[] into plain int[] (this is fine for tests because LTC dynamic shapes never actually worked); but this will need to be fixed for proper LTC SymInt support. The LTC codegen also looked somewhat questionable; I added comments based on my code reading.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83628
Approved by: https://github.com/albanD, https://github.com/bdhirsh
Summary:
When enabled, it will generate `torch_cuda_linalg` library, which would depend on cusolve and magma and registers dynamic bindings to it from LinearAlgebraStubs
Avoid symbol clashes that can result in infinite recursion by moving all symbols in the library to its own namespace.
Add checks that should prevent calling self in recursion to `LinearAlgebraStubs.cpp`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73447
Reviewed By: albanD
Differential Revision: D34538827
Pulled By: malfet
fbshipit-source-id: f2535b471d3524768a84b2e169b6aa24c26c03bf
(cherry picked from commit 4ec24b079c861c1122f0fa86e280b977c3c2f7ac)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72306
When enable, it will generate `torch_cuda_linalg` library, which would depend on cusolve and magma and registers dynamic bindings to it from LinearAlgebraStubs
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D33992795
Pulled By: malfet
fbshipit-source-id: d1fa351a320659b29754997c20d754e69bfe36c0
(cherry picked from commit d5d6c69a988b9454538ecd28674206da2541de17)
Summary:
Make `TORCH_CUDABLAS_CHECK` and `TORCH_CUSOLVER_CHECK` available in custom extensions by exporting the internal functions called by the both macros.
Rel: https://github.com/pytorch/pytorch/issues/67073
cc xwang233 ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67161
Reviewed By: jbschlosser
Differential Revision: D31984694
Pulled By: ngimel
fbshipit-source-id: 0035ecd1398078cf7d3abc23aaefda57aaa31106
Summary:
This PR implements the necessary hooks/stubs/enums/etc for complete ONNX Runtime (ORT) Eager Mode integration. The actual extension will live out of tree at https://github.com/pytorch/ort.
We have been [working on this at Microsoft](https://github.com/microsoft/onnxruntime-pytorch/tree/eager-ort/torch_onnxruntime) for the last few months, and are finally ready to contribute the PyTorch core changes upstream (nothing major or exciting, just the usual boilerplate for adding new backends).
The ORT backend will allow us to ferry [almost] all torch ops into granular ONNX kernels that ORT will eagerly execute against any devices it supports (therefore, we only need a single ORT backend from a PyTorch perspective).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58248
Reviewed By: astaff
Differential Revision: D30344992
Pulled By: albanD
fbshipit-source-id: 69082b32121246340d686e16653626114b7714b2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61104
This patch added a new test case for findDanglingImpls. The test case introduces a C++ extension which has a dangling impl such that findDanglingImpls can find it and output its information.
Test Plan:
python test/test_dispatch.py TestDispatch.test_find_dangling_impls_ext
Imported from OSS
Reviewed By: ezyang
Differential Revision: D29512520
fbshipit-source-id: 6883fb8f065f2c0ae0e7a1adf6fd298591497e2b
Summary:
The function name and return type both are called `class_`, therefore they are ambiguous and this is UB and does not work on NVCC. See the tests for the failure case.
Thanks for the help of Thibaut Lutz from NVIDIA's compiler team.
cc: yueyericardo ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57962
Reviewed By: mruberry
Differential Revision: D28359400
Pulled By: ezyang
fbshipit-source-id: c64ec89203f99f656611aba34f7424eed7bc9e7c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53583
`Scalar` takes 32 bytes due to `c10::complex<double>`
requires aligning to 16 bytes. Passing Scalar by reference
shows about 1% improvements on instruction count.
All the changes in this commit are codemoded except for
the following 4 files (which code-gen signatures):
```
tools/codegen/api/cpp.py
tools/codegen/api/native.py
tools/codegen/api/structured.py
caffe2/contrib/aten/gen_op.py
```
# Codemode
## Main Step
For the codemod part, here is the main command used:
```
fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}'
fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)optional<Scalar> (\w+)' '${1}const optional<Scalar>& ${2}'
fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}'
fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)optional<Scalar> (\w+)' '${1}const optional<Scalar>& ${2}'
```
As you can tell, it codemods both `Scalar` and `optional<Scalar>`. Apply these commands iteratively until reaching a fix-point (since one method signature might contain multiple `Scalar` parameter).
In retrospect, excluding `thrid_party` and `torch/csrc/jit` would be a good idea. (I revert it manually later, see https://github.com/pytorch/pytorch/pull/53479 as an reference).
## Pre-Step
Prior to applying the main command, as some `Scalar` are presented as `at::Scalar` or `c10::Scalar`, so I codemod some of them in advance. Here is an incomplete list:
```
fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)at::Scalar (\w+)' '${1}const at::Scalar& ${2}'
fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)at::Scalar (\w+)' '${1}const at::Scalar& ${2}'
fastmod --extensions h '([a-zA-Z_+]\([^)]*,?\s*)c10::optional<Scalar> (\w+)' '${1}const c10::optional<Scalar>& ${2}'
fastmod --extensions cpp '([a-zA-Z_+]\([^)]*,?\s*)c10::optional<Scalar> (\w+)' '${1}const c10::optional<Scalar>& ${2}'
```
## Fixup
There are a couple of post codemod fixup. For example, `const Scalar` will be codemoded into `const const Scalar&`. `at:Scalar` will be codemoded into `at::const Scalar&` (if `Pre-step` is not done comprehensively). Here is an incomplete list:
```
fastmod --extensions cpp 'const const Scalar' 'const Scalar'
fastmod --extensions h 'const const c10::optional<Scalar>' 'const c10::optional<Scalar>'
fastmod --extensions cpp 'const const c10::optional<Scalar>' 'const c10::optional<Scalar>'
fastmod 'at::const Scalar&' 'const at::Scalar&'
```
## Supplementary
`cu` and `mm` files also need to be codemoded, for example:
```
fastmod --extensions cu 'at::const Scalar&' 'const at::Scalar&'
fastmod --extensions mm '([a-zA-Z_+]\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}'
```
Function pointers are not codemoded. Here is an incomplete list:
```
# Cover case: using index_fill_fn = void(*)(TensorIterator & iter, int64_t dim, int64_t self_dim_size, int64_t self_dim_stride, Scalar source);
fastmod --extensions h '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)Scalar (\w+)' '${1}const Scalar& ${2}'
# Cover case: using softplus_fn = void (*)(TensorIterator&, Scalar, Scalar);
fastmod --extensions h '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)Scalar([, \)])' '${1}const Scalar&${2}'
fastmod --extensions cpp '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)Scalar([, \)])' '${1}const Scalar&${2}'
fastmod --extensions h '(void\s*\(\s*\*\s*\)\([^)]*,?\s*)optional<Scalar>([, \)])' '${1}const optional<Scalar>&${2}'
```
Some corner cases needs to be manually fixed.
ghstack-source-id: 123970306
Test Plan: Imported from OSS
Reviewed By: smessmer
Differential Revision: D26904445
fbshipit-source-id: 8d8a002af4b5125f153a32f03c6956be7ae5671d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53037
As remarked in #52277 it is easy to give an (inefficient, due to extra
redispatches) DefaultBackend implementation of foo and foo_ in terms of
foo_out. This patch enables code generation for DefaultBackend in these
cases by default for all structured kernels. You can see the payoff
in MSNPU extension: it only has to register a kernel for add.out, and it
gets add and add_ kernels automatically.
The actual code changes are very modest:
- When DefaultBackend, call the dispatched (not direct native::)
functions to allocate tensors, change device guard, etc
- Don't call impl() for DefaultBackend (as it doesn't exist); instead,
directly generate a call to at::foo_out to do the actual work.
- Do NOT generate DefaultBackend implementation for foo_out. Actually,
there is a case to be made for this being a good idea with more infra;
see comments inside.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D26731225
Pulled By: ezyang
fbshipit-source-id: 939da7cb69f694722ec293e5e42e74a755dd0985
Summary:
## Rationale
While most of the `torch.Generator` properties and methods are implemented as a thin wrapper of the corresponding `at::Generator` methods, `torch.Generator.get_state()` and `torch.Generator.set_state()` are implemented in legacy Torch code and are not dispatched through the `c10::GeneratorImpl` interface. This is not structured well and makes implementing generators for new backends (e.g. `XLAGeneratorImpl` for the XLA backend) inconvenient. As such, this pull request seeks to move these generator state APIs to c10 and ATen.
## What is being refactored?
* Interfaces
- Added `c10::GeneratorImpl::set_state` and `c10::GeneratorImpl::state` for getting and setting the internal state of a random number generator.
- `at::Generator::set_state` and `at::Generator::state` wraps the above-mentioned APIs, as it's basically a PIMPL.
- Added helper function `at::detail::check_rng_state` for checking the validity of new RNG state tensor.
* CPU Generator
- Renamed and moved `THTensor_(setRNGState)` and `THTensor_(getRNGState)` to `CPUGeneratorImpl::set_state` and `CPUGenerator::state`.
- Renamed and moved `THGeneratorState` and `THGeneratorStateNew` to `CPUGeneratorStateLegacy` and `CPUGeneratorState`.
* CUDA Generator
- Renamed and moved `THCRandom_setRNGState` and `THCRandom_getRNGState` to `CUDAGeneratorImpl::set_state` and `CUDAGeneratorImpl::state`.
* PyTorch Bindings
- `THPGenerator_setState` and `THPGenerator_getState` now simply forward to `at::Generator::set_state` and `at::Generator::state`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49589
Reviewed By: H-Huang
Differential Revision: D25785774
Pulled By: pbelevich
fbshipit-source-id: 8ed79209c4ffb1a0ae8b19952ac8871ac9e0255f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49220
Since all ops are c10-full, we can remove .impl_UNBOXED now.
This also removes the ability of KernelFunction or CppFunction to store unboxedOnly kernels.
ghstack-source-id: 119450489
Test Plan: waitforsandcastle
Reviewed By: ezyang
Differential Revision: D25490225
fbshipit-source-id: 32de9d591e6a842fe18abc82541580647e9cfdad
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49145
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49105
(1) Add a safety check `C10_CUDA_KERNEL_LAUNCH_CHECK()` after each kernel launch. This diff only changes the files inside the directory /fbsource/fbcode/caffe2/modules/, /fbsource/fbcode/caffe2/fb/, /fbsource/fbcode/caffe2/test/.
(2) Get rid of old check `AT_CUDA_CHECK(cudaGetLastError())` when necessary.
Test Plan:
Test build:
```
buck build mode/dev-nosan //caffe2/modules/detectron:
buck test mode/dev-nosan //caffe2/modules/detectron:
buck build mode/dev-nosan //caffe2/torch/fb/:
buck test mode/dev-nosan //caffe2/torch/fb/:
```
To check for launches without checks:
```
python3 caffe2/torch/testing/check_kernel_launches.py
```
Make sure none of the updated files are in the returned list.
Reviewed By: r-barnes
Differential Revision: D25452852
fbshipit-source-id: d6657edab612c9e0fa99b29c68460be8b1a20064
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49105
(1) Add a safety check `C10_CUDA_KERNEL_LAUNCH_CHECK()` after each kernel launch. This diff only changes the files inside the directory /fbsource/fbcode/caffe2/modules/, /fbsource/fbcode/caffe2/fb/, /fbsource/fbcode/caffe2/test/.
(2) Get rid of old check `AT_CUDA_CHECK(cudaGetLastError())` when necessary.
Test Plan:
Test build:
```
buck build //caffe2/modules/detectron:
buck build //caffe2/torch/fb/:
```
To check for launches without checks:
```
python3 caffe2/torch/testing/check_kernel_launches.py
```
Make sure none of the updated files are in the returned list.
Reviewed By: r-barnes
Differential Revision: D25325039
fbshipit-source-id: 2043d6e63c7d029c35576d3101c18247ffe92f01
Summary:
[Refiled version of earlier PR https://github.com/pytorch/pytorch/issues/45451]
This PR revamps the hipify module in PyTorch to overcome a long list of shortcomings in the original implementation. However, these improvements are applied only when using hipify to build PyTorch extensions, not for PyTorch or Caffe2 itself.
Correspondingly, changes are made to cpp_extension.py to match these improvements.
The list of improvements to hipify is as follows:
1. Hipify files in the same directory as the original file, unless there's a "cuda" subdirectory in the original file path, in which case the hipified file will be in the corresponding file path with "hip" subdirectory instead of "cuda".
2. Never hipify the file in-place if changes are introduced due to hipification i.e. always ensure the hipified file either resides in a different folder or has a different filename compared to the original file.
3. Prevent re-hipification of already hipified files. This avoids creation of unnecessary "hip/hip" etc. subdirectories and additional files which have no actual use.
4. Do not write out hipified versions of files if they are identical to the original file. This results in a cleaner output directory, with minimal number of hipified files created.
5. Update header rewrite logic so that it accounts for the previous improvement.
6. Update header rewrite logic so it respects the rules for finding header files depending on whether "" or <> is used.
7. Return a dictionary of mappings of original file paths to hipified file paths from hipify function.
8. Introduce a version for hipify module to allow extensions to contain back-compatible code that targets a specific point in PyTorch where the hipify functionality changed.
9. Update cuda_to_hip_mappings.py to account for the ROCm component subdirectories inside /opt/rocm/include. This also results in cleanup of the Caffe2_HIP_INCLUDE path to remove unnecessary additions to the include path.
The list of changes to cpp_extension.py is as follows:
1. Call hipify when building a CUDAExtension for ROCm.
2. Prune the list of source files to CUDAExtension to include only the hipified versions of any source files in the list (if both original and hipified versions of the source file are in the list)
3. Add subdirectories of /opt/rocm/include to the include path for extensions, so that ROCm headers for subcomponent libraries are found automatically
cc jeffdaily sunway513 ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48715
Reviewed By: bdhirsh
Differential Revision: D25272824
Pulled By: ezyang
fbshipit-source-id: 8bba68b27e41ca742781e1c4d7b07c6f985f040e
Summary:
This PR revamps the hipify module in PyTorch to overcome a long list of shortcomings in the original implementation. However, these improvements are applied only when using hipify to build PyTorch extensions, **not for PyTorch or Caffe2 itself**.
Correspondingly, changes are made to `cpp_extension.py` to match these improvements.
The list of improvements to hipify is as follows:
1. Hipify files in the same directory as the original file, unless there's a "cuda" subdirectory in the original file path, in which case the hipified file will be in the corresponding file path with "hip" subdirectory instead of "cuda".
2. Never hipify the file in-place if changes are introduced due to hipification i.e. always ensure the hipified file either resides in a different folder or has a different filename compared to the original file.
3. Prevent re-hipification of already hipified files. This avoids creation of unnecessary "hip/hip" etc. subdirectories and additional files which have no actual use.
4. Do not write out hipified versions of files if they are identical to the original file. This results in a cleaner output directory, with minimal number of hipified files created.
5. Update header rewrite logic so that it accounts for the previous improvement.
6. Update header rewrite logic so it respects the rules for finding header files depending on whether `""` or `<>` is used.
7. Return a dictionary of mappings of original file paths to hipified file paths from `hipify` function.
8. Introduce a version for hipify module to allow extensions to contain back-compatible code that targets a specific point in PyTorch where the hipify functionality changed.
9. Update `cuda_to_hip_mappings.py` to account for the ROCm component subdirectories inside `/opt/rocm/include`. This also results in cleanup of the `Caffe2_HIP_INCLUDE` path to remove unnecessary additions to the include path.
The list of changes to `cpp_extension.py` is as follows:
1. Call `hipify` when building a CUDAExtension for ROCm.
2. Prune the list of source files to CUDAExtension to include only the hipified versions of any source files in the list (if both original and hipified versions of the source file are in the list)
3. Add subdirectories of /opt/rocm/include to the include path for extensions, so that ROCm headers for subcomponent libraries are found automatically
cc jeffdaily sunway513 hgaspar lcskrishna ashishfarmer
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45451
Reviewed By: ezyang
Differential Revision: D24924736
Pulled By: malfet
fbshipit-source-id: 4af42b8ff4f21c3782dedb8719b8f9f86b34bd2d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46092
Make empty c10-full without using hacky-wrapper, i.e. port the kernel to the new style signature.
This PR also changes the signature of some helpers called by empty to the new style.
ghstack-source-id: 116544203
(Note: this ignores all push blocking failures!)
Test Plan:
vs prev diff (outdated, before c10::optional fix): https://www.internalfb.com/intern/fblearner/details/224735103/
after c10::optional fix:
https://www.internalfb.com/intern/fblearner/details/231391773/
Also, after the c10::optional fix, the instruction counting benchmark shows a 2% regression for calling empty from Python. We decided this is acceptable and decided against landing D24425836 which would fix the regression.
Reviewed By: ezyang
Differential Revision: D24219944
fbshipit-source-id: e554096e90ce438c75b679131c3151ff8e5c5d50
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45181
`init_process_group` and `new_group` update a bunch of global
variables after initializing the actual process group. As a result, there is a
race that after initializing the process group on say rank 0, if we immediately
check the default process group on rank 1 (say via RPC), we might actually get
an error since rank 1 hasn't yet updated its _default_pg variable.
To resolve this issue, I've added barrier() at the end of both of these calls.
This ensures that once these calls return we are guaranteed about correct
initialization on all ranks.
Since these calls are usually done mostly during initialization, it should be
fine to add the overhead of a barrier() here.
#Closes: https://github.com/pytorch/pytorch/issues/40434, https://github.com/pytorch/pytorch/issues/40378
ghstack-source-id: 112923112
Test Plan:
Reproduced the failures in
https://github.com/pytorch/pytorch/issues/40434 and
https://github.com/pytorch/pytorch/issues/40378 and verified that this PR fixes
the issue.
Reviewed By: mrshenli
Differential Revision: D23858025
fbshipit-source-id: c4d5e46c2157981caf3ba1525dec5310dcbc1830
